Skip to content

IRCVLab/AUE8088-PA2

Repository files navigation

HYU-AUE8088, Understanding and Utilizing Deep Learning

PA #2. Object Detection

Important Files

├── README.md
├── requirements.txt
├── datasets
│   └── nuscenes/ (see below explanation)
├── data
│   ├── ...
│   └── nuscenes.yaml
├── models
│   ├── ...
│   ├── yolo.py
│   └── yolo5n_nuscenes.yaml
├── utils
│   ├── ...
│   ├── dataloaders.py
│   └── loss.py
├── detect.py
├── debug.ipynb
└── train_simple.py

Preparation

  • Prepare dataset (4.3GB, resized images with bbox labels, front camera only)

    $ wget https://hyu-aue8088.s3.ap-northeast-2.amazonaws.com/nuscenes_det2d.tar.gz
    $ tar xzvf nuscenes_det2d.tar.gz
  • Create python virtual environment

    $ python3 -m venv venv/aue8088-pa2
    $ source venv/aue8088-pa2/bin/activate
  • Check whether the virtual environment set properly : The result should end with venv/aue8088-pa2/bin/python.

    $ which python
  • Clone base code repository (replace ircvlab to your account if you forked the repository)

    $ git clone https://github.com/ircvlab/aue8088-pa2
  • [!] Create a symbolic link for nuscenes dataset

    • Assume the below folder structure

      ├── nuscenes_det2d
      ├── aue8088-pa2
      │   ├── data/
      │   ├── models/
      │   ├── train_simple.py
      │   ├── ...
      │   └── README.md (this file)
    • Follow below commands

      $ cd aue8088-pa2
      $ mkdir datasets
      $ ln -s $(realpath ../nuscenes_det2d) datasets/nuscenes
      $
  • Install required packages

    $ pip install -r requirements.txt

Train

  • Command
    $ python train_simple.py \
      --img 416 \
      --batch-size 64 \
      --epochs 40 \
      --data data/nuscenes.yaml \
      --cfg models/yolov5n_nuscenes.yaml \
      --weights yolov5n.pt \
      --workers 16 \
      --name yolov5n

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published